[CLEANUP]

README.md

@@ -8,6 +8,24 @@ The idea for WhiteRock emerged during a pivotal event that underscored the ineff
 ## Install
 `$ pip3 install whiterock`
 
+## Usage
+```python
+from whiterock.main import WhiteRock
+from whiterock.agents import due_diligence_agent, principal_investor
+
+# Instantiate the WhiteRock class
+whiterock = WhiteRock(
+    agents=[due_diligence_agent, principal_investor],
+    max_loops=5,
+    phone_number="+16505188709",  ##+19729719060",
+    phone_call_duration=160,
+)
+
+# Run the WhiteRock class
+task = "Enter in your task"
+whiterock.run(task)
+```
+
 ## What It Does
 
 WhiteRock is designed to transform the way venture capital operates by automating the entire investment process. Here’s how it works:

example.py

@@ -0,0 +1,14 @@
+from whiterock.main import WhiteRock
+from whiterock.agents import due_diligence_agent, principal_investor
+
+# Instantiate the WhiteRock class
+whiterock = WhiteRock(
+    agents=[due_diligence_agent, principal_investor],
+    max_loops=5,
+    phone_number="+16505188709",  ##+19729719060",
+    phone_call_duration=160,
+)
+
+# Run the WhiteRock class
+task = "Enter in your task"
+whiterock.run(task)

whiterock/agents.py

@@ -0,0 +1,87 @@
+import os
+
+from dotenv import load_dotenv
+from swarms import Agent
+
+from swarms import OpenAIChat
+from whiterock.memory import ChromaDB
+from whiterock.prompts import (
+    PRINCIPAL_SYSTEM_PROMPT,
+    due_diligence_agent_system_prompt,
+)
+
+# Load the environment variables
+load_dotenv()
+
+# Memory
+memory = ChromaDB(
+    output_dir="whiterock",
+    n_results=1,
+)
+
+# GROQ API key
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# GROQ LLM
+model = OpenAIChat(
+    openai_api_key=os.getenv("OPENAI_API_KEY"),
+    max_tokens=3000,
+    temperature=0.2,
+)
+
+
+agent_names = [
+    "Due Diligence Agent",
+    "Principal Investor Agent",
+]
+
+system_prompts = [
+    due_diligence_agent_system_prompt(),
+    PRINCIPAL_SYSTEM_PROMPT(),
+]
+
+
+due_diligence_agent = Agent(
+    agent_name=agent_names[0],
+    system_prompt=system_prompts[0],
+    agent_description=system_prompts[0],
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    # dashboard=False,
+    # verbose=True,
+    # interactive=True,
+    # interactive=True,
+    state_save_file_type="json",
+    saved_state_path=f"{agent_names[0].lower().replace(' ', '_')}.json",
+    # docs_folder="data", # Folder of docs to parse and add to the agent's memory
+    # long_term_memory=memory,
+    # dynamic_temperature_enabled=True,
+    # pdf_path="docs/medical_papers.pdf",
+    # list_of_pdf=["docs/medical_papers.pdf", "docs/medical_papers_2.pdf"],
+    # docs=["docs/medicalx_papers.pdf", "docs/medical_papers_2.txt"],
+    # memory_chunk_size=2000,
+)
+
+
+principal_investor = Agent(
+    agent_name=agent_names[1],
+    system_prompt=system_prompts[1],
+    agent_description=system_prompts[1],
+    llm=model,
+    max_loops=1,
+    autosave=True,
+    # dashboard=False,
+    # verbose=True,
+    # interactive=True,
+    # interactive=True,
+    state_save_file_type="json",
+    saved_state_path=f"{agent_names[0].lower().replace(' ', '_')}.json",
+    # docs_folder="data", # Folder of docs to parse and add to the agent's memory
+    # long_term_memory=memory,
+    # dynamic_temperature_enabled=True,
+    # pdf_path="docs/medical_papers.pdf",
+    # list_of_pdf=["docs/medical_papers.pdf", "docs/medical_papers_2.pdf"],
+    # docs=["docs/medicalx_papers.pdf", "docs/medical_papers_2.txt"],
+    # memory_chunk_size=2000,
+)

whiterock/groq_llm.py

@@ -0,0 +1,74 @@
+from groq import Groq
+from swarms import BaseLLM
+
+
+class GroqChat(BaseLLM):
+    """
+    A client for interacting with the Groq API.
+
+    Args:
+        system_prompt (str, optional): The system prompt to use for generating completions. Defaults to None.
+        model_name (str, optional): The name of the model to use for generating completions. Defaults to "llama3-8b-8192".
+        temperature (float, optional): The temperature parameter for generating completions. Defaults to 0.5.
+        max_tokens (int, optional): The maximum number of tokens in the generated completions. Defaults to 1024.
+        top_p (float, optional): The top-p parameter for generating completions. Defaults to 1.
+        stop (str, optional): The stop sequence to use for generating completions. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        groq_api_key: str = None,
+        system_prompt: str = None,
+        model_name: str = "llama3-8b-8192",
+        temperature: float = 0.5,
+        max_tokens: int = 1024,
+        top_p: float = 1,
+        stop: str = None,
+        *args,
+        **kwargs,
+    ):
+        self.groq_api_key = groq_api_key
+        self.system_prompt = system_prompt
+        self.model_name = model_name
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self.top_p = top_p
+        self.stop = stop
+
+        self.client = Groq(api_key=self.groq_api_key, *args, **kwargs)
+
+    def run(self, task: str = None, *args, **kwargs):
+
+        output = self.client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": self.system_prompt,
+                },
+                {
+                    "role": "user",
+                    "content": task,
+                },
+            ],
+            model=self.model_name,
+            temperature=self.temperature,
+            max_tokens=self.max_tokens,
+            top_p=self.top_p,
+            stop=self.stop,
+            *args,
+            **kwargs,
+        )
+
+        output = output.choices[0].message.content
+        return output
+
+
+# # Model
+# model = GroqChat(
+#     groq_api_key="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",
+#     system_prompt="Welcome to GroqChat! How can I assist you today?",
+# )
+
+# # Run
+# out = model.run(task="What is the capital of France?")
+# print(out)

whiterock/main.py

@@ -0,0 +1,115 @@
+from swarms import BaseSwarm, Agent
+from typing import List
+from swarms import Conversation
+from whiterock.agents import principal_investor, due_diligence_agent
+from whiterock.prompts import analyst_caller_agent
+from whiterock.tools import call_api, fetch_transcription
+
+class WhiteRock(BaseSwarm):
+    """
+    WhiteRock class represents a swarm of agents involved in a due diligence process.
+
+    Args:
+        agents (List[Agent]): List of agents participating in the swarm. Defaults to vc_agents.
+        max_loops (int): Maximum number of loops the swarm will run. Defaults to 5.
+        phone_number (str): Phone number used for making phone calls. Defaults to "+17866955339".
+        phone_call_duration (int): Maximum duration of a phone call in seconds. Defaults to 160.
+        vc_rules (str): Rules for the conversation. Defaults to None.
+        *args: Variable length argument list.
+        **kwargs: Arbitrary keyword arguments.
+
+    Attributes:
+        agents (List[Agent]): List of agents participating in the swarm.
+        max_loops (int): Maximum number of loops the swarm will run.
+        phone_number (str): Phone number used for making phone calls.
+        phone_call_duration (int): Maximum duration of a phone call in seconds.
+        history (Conversation): Conversation history object.
+
+    Methods:
+        run(task: str, *args, **kwargs) -> Any: Runs the swarm and returns the output.
+
+    """
+
+    def __init__(
+        self,
+        agents: List[Agent] = None,
+        max_loops: int = 5,
+        phone_number: str = "+17866955339",
+        phone_call_duration: int = 160,
+        vc_rules: str = None,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.agents = agents
+        self.max_loops = max_loops
+        self.phone_number = phone_number
+        self.phone_call_duration = phone_call_duration
+
+        # History
+        self.history = Conversation(
+            time_enabled=True,
+            auto_save=True,
+            rules=vc_rules,
+        )
+
+    def run(self, task: str, *args, **kwargs):
+        """
+        Runs the swarm and returns the output.
+
+        Args:
+            task (str): The task to be performed by the swarm.
+            *args: Variable length argument list.
+            **kwargs: Arbitrary keyword arguments.
+
+        Returns:
+            Any: The output of the swarm.
+
+        """
+        loop = 0
+
+        for _ in range(self.max_loops):
+            # Combine strings into one
+            out = f"{analyst_caller_agent()} {task}"
+
+            transcription = call_api(
+                self.phone_number,
+                max_duration=self.phone_call_duration,
+                prompt=out,
+            )
+
+            out = fetch_transcription(transcription, max_duration=self.phone_call_duration)
+
+            self.history.add(
+                role="Due Diligence Agent",
+                content=out,
+            )
+
+            # Due Diligence Agent
+            due_diligence_agent = self.agents[0]
+            out = due_diligence_agent.run(
+                self.history.return_history_as_string(), *args, **kwargs
+            )
+            print(out)
+            self.history.add(
+                role="Due Diligence Agent",
+                content=out,
+            )
+            # Function call to mercury, add docs into a folder so rag can pick it up
+            # Function call to rag, add perplexity agent to the rag agent so it can search the web for more information
+
+            # Principal Investor Agent
+            principal_investor_agent = self.agents[1]
+            out = principal_investor_agent.run(
+                self.history.return_history_as_string(), *args, **kwargs
+            )
+            print(out)
+            self.history.add(
+                role="Principal Investor Agent",
+                content=out,
+            )
+
+            loop += 1
+
+        return out
+